Dynamic ventilation control for a building

ABSTRACT

Appropriate ventilation for a building space while maintaining building comfort includes tracking one or more interior environmental conditions within the building space and one or more exterior environmental conditions outside of the building space during operation of the HVAC system. An environmental model for the building space is learned over time based at least in part on these tracked environmental conditions, where the environmental model predicts an environmental state of the building space in response to operation of the HVAC system under various interior and exterior environmental conditions. A maximum allowed ventilation rate that can be achieved without causing the HVAC system to compromise on any of one or more comfort conditions of the building space is predicted using the environmental model. The outdoor air ventilation damper of the HVAC system is then controlled to provide an appropriate ventilation up to or at the predicted maximum allowed ventilation rate.

RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119 toU.S. Provisional Application Ser. No. 63/137,526, filed Jan. 14, 2021,the entirety of which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to control of Heating,Ventilating and Air Conditioning (HVAC) systems. More particularly, thepresent disclosure relates to methods and systems of determiningventilation rates for an HVAC system.

BACKGROUND

HVAC systems provide conditioned air for heating and cooling theinterior of a building. Some HVAC systems also can provide fresh airventilation into the building while exhausting an equivalent amount ofinside air. Such fresh air ventilation is useful in reducingcontaminates produced in the building. However, there are often costsinvolved in conditioning the fresh air before it can be deployed in thebuilding. For example, in the winter, the fresh air must typically beheated by the HVAC system, and in some cases, humidity must be added.Likewise, in the summer, the fresh air must typically be cooled by theHVAC system, and in some cases, humidity must be removed. Thus, toreduce operating costs, it is often desirable to minimize theventilation rate while still adequately ventilating the building giventhe current contaminates or expected contaminates in the building.

Under some conditions, such as during a pandemic, it may be desirable toprioritize an increased ventilation rate over energy costs to helpreduce the spread of pathogens within the building. Under theseconditions, if the ventilation rate is set too high, given the currentindoor and outdoor conditions, the HVAC system may lack the heatingand/or cooling capacity to adequately condition the incoming fresh airwhile still maintaining occupant comfort in the building.

What would be desirable are methods and systems for automaticallydetermining a ventilation rate for a building, given the current indoorand outdoor conditions, and dynamically controlling the HVAC system inaccordance with the determined ventilation rate.

SUMMARY

The present disclosure relates to methods and systems of determiningventilation rates for an HVAC system. In one example, a method providesup to a maximum ventilation for a building space while not compromisingon one or more comfort conditions in the building space. Thisillustrative method includes tracking one or more interior environmentalconditions within the building space and one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system. An environmental model for the building space islearned over time based at least in part on the tracked one or moreinterior environmental conditions within the building space and the oneor more exterior environmental conditions outside of the building spaceduring operation of the HVAC system. The learned environmental model isconfigured to predict an environmental state of the building space inresponse to operation of the HVAC system under various interior andexterior environmental conditions. A current maximum allowed ventilationrate that can be achieved without causing the HVAC system to compromiseon any of the one or more comfort parameters of the building space ispredicted. This may include inputting to the learned environmental modelone or more current comfort parameters associated with the one or morecomfort conditions of the building space, one or more current interiorenvironmental conditions, and one or more current exterior environmentalconditions. Once the current maximum allowed ventilation rate ispredicted, an outdoor air ventilation damper of the HVAC system iscontrolled to provide ventilation up to or at the current maximumallowed ventilation rate.

In another example, a method provides a dynamic ventilation rate for abuilding space using a Heating, Ventilating and/or Air Conditioning(HVAC) system. This illustrative method includes tracking over time oneor more interior environmental conditions within the building space andone or more exterior environmental conditions outside of the buildingspace during operation of the HVAC system. An environmental model forthe building space is learned over time based at least in part on thetracked one or more interior environmental conditions and the one ormore exterior environmental conditions during operation of the HVACsystem. The learned environmental model is configured to predict anenvironmental state of the building space in response to operation ofthe HVAC system under various interior and exterior environmentalconditions. A dynamic ventilation rate for the HVAC system of thebuilding space is determined based at least in part on inputting to theenvironmental model of the building space one or more interiorenvironmental conditions and one or more exterior environmentalconditions, wherein the determined dynamic ventilation rate is capped toallow the HVAC system to not compromise on one or more comfortconditions in the building space. An outdoor air ventilation damper ofthe HVAC system is then controlled in accordance with the determineddynamic ventilation rate.

In another example, a method for controlling an outdoor air ventilationdamper of a Heating, Ventilating and/or Air Conditioning (HVAC) systemserving a building space of a building is provided. The illustrativemethod include receiving one or more interior environmental conditionswithin the building space, one or more exterior environmental conditionsoutside of the building space, and one or more operating conditions ofthe HVAC system over time. A measure of current unused heating and/orcooling capacity of the HVAC system is determined based at least in parton the one or more operating conditions of the HVAC system. A maximumventilation parameter is determined that is representative of a rate ofoutside air having the one or more exterior environmental conditionsthat can be conditioned by the measure of current unused heating and/orcooling capacity of the HVAC system while still maintaining the one ormore comfort conditions for the building space. A ventilation rate isdetermined based at least in part on the maximum ventilation parameter,and the determined ventilation rate is sent for use in controlling theoutdoor air ventilation damper of the HVAC system.

The preceding summary is provided to facilitate an understanding of someof the innovative features unique to the present disclosure and is notintended to be a full description. A full appreciation of the disclosurecan be gained by taking the entire specification, claims, figures, andabstract as a whole.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure may be more completely understood in consideration of thefollowing description of various examples in connection with theaccompanying drawings, in which:

FIG. 1 is a schematic block diagram of an illustrative HVAC controlsystem;

FIG. 2 is a flow diagram showing an illustrative method of providing amaximum allowed ventilation for a building space;

FIG. 3 is a flow diagram showing an illustrative method of providing amaximum allowed ventilation for a building space;

FIG. 4 is a flow diagram showing an illustrative method of providing amaximum allowed ventilation for a building space;

FIG. 5 is a flow diagram showing an illustrative method of providing adynamic ventilation rate for a building space;

FIG. 6 is a flow diagram showing an illustrative method of providing adynamic ventilation rate for a building space;

FIG. 7 is a flow diagram showing an illustrative method of providing adynamic ventilation rate for a building space;

FIG. 8 is a schematic block diagram showing an illustrative HVAC controlsystem;

FIG. 9 is a schematic block diagram of an illustrative algorithm formingpart of the illustrative HVAC control system of FIG. 8 ;

FIG. 10 is a graph showing an example of energy penalty weightingrelative to air flow rate;

FIG. 11 is a graph showing an example of energy penalty weightingrelative to cumulative IAQ concentration;

FIG. 12 is a graph showing an example of a Heaviside function;

FIG. 13 is a flow diagram showing an illustrative method;

FIG. 14 is a flow diagram showing an illustrative method;

FIG. 15 is a flow diagram showing an illustrative method;

FIG. 16 is a schematic view of an illustrative MPC architecture forbuilding control; and

FIG. 17 is a graphical representation of how a building model mayinclude a non-linear representation of how one or more environmentalparameters associated with the building space is predicted to respond tochanges in HVAC system operation under a plurality of differentoperating conditions.

While the disclosure is amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the disclosureto the particular examples described. On the contrary, the intention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the disclosure.

DESCRIPTION

The following description should be read with reference to the drawings,in which like elements in different drawings are numbered in likefashion. The drawings, which are not necessarily to scale, depictexamples that are not intended to limit the scope of the disclosure.Although examples are illustrated for the various elements, thoseskilled in the art will recognize that many of the examples providedhave suitable alternatives that may be utilized.

All numbers are herein assumed to be modified by the term “about”,unless the content clearly dictates otherwise. The recitation ofnumerical ranges by endpoints includes all numbers subsumed within thatrange (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5).

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include the plural referents unless thecontent clearly dictates otherwise. As used in this specification andthe appended claims, the term “or” is generally employed in its senseincluding “and/or” unless the content clearly dictates otherwise.

It is noted that references in the specification to “an embodiment”,“some embodiments”, “other embodiments”, etc., indicate that theembodiment described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it is contemplated that the feature,structure, or characteristic is described in connection with anembodiment, it is contemplated that the feature, structure, orcharacteristic may be applied to other embodiments whether or notexplicitly described unless clearly stated to the contrary.

FIG. 1 is a schematic block diagram of an illustrative HVAC controlsystem 10. In the example shown, a building space 12 includes acontroller 14 that is configured to control at least some features andoperations of an HVAC system 16. The building space 12 may represent theinterior of an entire building, or only part of a building. Thecontroller 14 may control operation of a damper 18 that is part of theHVAC system 16 and that functions to control the relative flow of freshoutside air into the building space 12 through the ductwork (not shown)that provides conditioned air to various parts of the building space 12.The controller 14 may control other features and components of the HVACsystem 16 as well. The controller 14 may operate in accordance withvarious HVAC standards such as but not limited to ASHRAE 62.1 to provideappropriate volumes of fresh air to the building space 12. Providingfresh air can provide the interior of the building space 12 withhealthier air that contains relatively less of various contaminants thanthe interior air in the building space 12 would otherwise have, asoutdoor air can be substantially cleaner than indoor air. Providingfresh air can also help with comfort, such as if the building space 12is currently warmer than a temperature setpoint but the outside air iscool enough that it can be used to help cool the building space 12 downto its temperature setpoint. This is just an example.

The illustrative controller 14 includes an input 20 for receiving one ormore interior environmental conditions within the building space 12 aswell as for receiving one or more exterior environmental conditionsoutside of the building space 12. The input 20 may also receive one ormore operating conditions of the HVAC system 16 over time. In theexample shown, the controller 14 includes a processor 22 that isoperatively coupled to the input 20 such that the processor 22 can trackover time one or more environmental conditions within the building space12 and also track over time one or more exterior environmentalconditions outside of the building space 12. The processor 22 may alsotrack one or more operating conditions of the HVAC system 16 over time,and correlate in time the one or more operating conditions of the HVACsystem 16 with the one or more environmental conditions within thebuilding space 12, the one or more exterior environmental conditionsoutside of the building space 12, and/or any other suitable conditionsor parameters. While a single processor 22 is shown, it will beappreciated that the controller 14 may include two, three or moredistinct processors 22. In cases where the controller 14 includesmultiple processors 22, the functionality of the controller 14 may bedivided between the two, three or more distinct processors 22, and insome cases, may be distributed amount a plurality of differentlocations.

In the example shown, the processor 22 may be configured to learn anenvironmental model for the building space 12 based at least in part onthe tracked one or more interior environmental conditions within thebuilding space 12 and the one or more exterior environmental conditionsoutside of the building space 12 during operation of the HVAC system 16.The learned environmental model is configured to predict anenvironmental state of the building space 12 in response to operation ofthe HVAC system 16 under various interior and exterior environmentalconditions. The processor 22 may be configured to determine a dynamicventilation rate for the HVAC system 16 of the building space 12 basedat least in part on inputting to the environmental model of the buildingspace 12 one or more current interior environmental conditions and oneor more current exterior environmental conditions. The illustrativecontroller 14 further includes an output 24 for sending the determineddynamic ventilation rate to the HVAC system 16 for controlling theoutdoor air ventilation damper 18 of the HVAC system 16.

In some cases, the processor 22 is configured to predict a currentmaximum allowed ventilation rate that can be achieved without causingthe HVAC system 26 to compromise on any of one or more comfortconditions of the building space. The HVAC system 16 may control theoutdoor air ventilation damper 18 to provide ventilation up to or at thecurrent maximum allowed ventilation rate.

In the example shown, the building space 12 may include one or moresensors 26, individually labeled as 26 a and 26 b. While two sensors 26are shown, it will be appreciated that this is merely illustrative, asthe building space 12 may include any number of sensors 26, and mayinclude only one sensor 26 or may include three, four, five or evensubstantially more sensors 26. At least some of the sensors 26 may behard-wired to the input 20. At least some of the sensors 26 may bewirelessly coupled to the input 20. The sensors 26 may represent any ofa variety of different types of sensors. The sensors 26 may beconfigured to provide signals representing one or more interiorenvironmental conditions to the input 20. The sensors 26 may includetemperature sensors, humidity sensors, CO₂ sensors and sensorsconfigured to detect other indoor pollutants such as particulate matter(PM), volatile organic compounds (VOCs) and the like. The sensors 26 mayinclude occupancy sensors, such as motion sensors, video camera sensorscoupled with video analytics that in some cases can identify andmaintain a count and/or density of people in the building space, a timeof flight (e.g. LIDAR) sensor that can detect and in some cases maintaina count and/or density of people in the building space, a milli-meterwave sensor (e.g. Radar) that can detect and in some cases maintain acount and/or density of people in the building space, and/or any othersuitable sensor as desired. People are known to produce contaminates inthe building space.

The illustrative HVAC control system 10 also includes one or moresensors 28 that are disposed outside of the building space 12 in orderto provide signals representing one or more exterior environmentalconditions to the input 20. At least some of the sensors 28 may behard-wired to the input 20. At least some of the sensors 28 may bewirelessly coupled to the input 20. In some cases, the sensors 28 areaccessed from a weather service via a suitable Application ProgrammingInterface (API). These are just examples. The sensors 28 may includetemperature sensors, humidity sensors, CO₂ sensors and sensorsconfigured to detect other pollutants such as particulate matter (PM),volatile organic compounds (VOCs) and the like.

In some instances, the controller 14 may communicate with a remoteserver 30. The remote server 30 may be a cloud-based server, forexample. An edge controller 32 may provide a go-between between thecontroller 14 and the remote server 30. The controller 14 may providedata to the remote server 30 for performance monitoring, for example. Asdiscussed thus far, the processor 22 within the controller 14 receivesvarious inputs from the interior sensors 26 and the exterior sensors 28,and may receive various inputs such as HVAC operational conditions fromthe HVAC system 16. The processor 22 may use these various inputs tolearn an environmental model for the building space 12, sometimes usingArtificial Intelligence and/or Machine Learning. In other cases, theprocessor 22 may simply receive the various inputs from the input 20 andforward the information to the output 24 for transmission to either theedge controller 32 itself or ultimately the remote server 30 forprocessing. In some cases, the processing power that monitors thevarious inputs and creates and maintains the learned environmental modelfor the building space 12 may reside within the edge controller 32. Insuch cases, the edge controller 32 may include one or more containers inwhich the processing power is manifested. In some cases, the edgecontroller 32 merely functions as a gateway, providing the informationto the remote server 30, where the processing power that monitors thevarious inputs and creates and maintains the learned environmental modelfor the building space 12 resides. In some cases, the processing powerthat monitors the various inputs and creates and maintains the learnedenvironmental model for the building space 12 is distributed throughoutthe HVAC control system 10, the edge controller 32 and/or the remoteserver 30. These are just examples.

In some cases, the learned environmental model is not static, but isrepeatedly updated to account for changes in the HVAC control system 10.These changes can include normal changes resulting from components ofthe HVAC control system 10 aging. An example is a filter that allows adecreasing air flow as the filter becomes clogged. Another example maybe a variation in fan speed caused by a belt that drives the fanstretching as it ages. Heat exchangers can lose efficiency over time.The building space 12 itself may change over time. For example, windowsmay start to leak additional air as weather stripping on the windowsages and contracts. Alternatively, window efficiency may increase if oldwindows are replaced. HVAC system efficiency may increase whenparticular parts of the HVAC system are replaced. These are justexamples of situations in which the learned environmental model isupdated to account for changes in the environment.

FIG. 2 is a flow diagram showing an illustrative method 40 for providinga maximum allowed ventilation rate for a building space (such as thebuilding space 12) while not compromising on one or more comfortconditions in the building space using a Heating, Ventilating and/or AirConditioning (HVAC) system that includes an outdoor air ventilationdamper (such as the HVAC system 16) of the building space. Theillustrative method 40 includes tracking one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system, as indicated at block 42. The one or moreinterior environmental conditions may include indoor air temperature.The one or more interior environmental conditions may include indoorhumidity. The one or more interior environmental conditions may includeconcentrations of one or more indoor pollutants such as but not limitedto CO₂, particular matter (PM 2.5 and PM 10) and volatile organiccompounds (VOCs). The one or more interior environmental conditions mayinclude occupancy such as a people count and/or a people density in thebuilding. The one or more exterior environmental conditions may includeoutdoor air temperature. The one or more exterior environmentalconditions may include outdoor humidity. The one or more exteriorenvironmental conditions may include one or more outdoor pollutants.These are just examples.

An environmental model for the building space is learned over time basedat least in part on the tracked one or more interior environmentalconditions within the building space and the one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system, as indicated at block 44. In some cases, theenvironmental model is learned using Artificial Intelligence and/orMachine Learning. The learned environmental model is configured to anenvironmental state of the building space in response to operation ofthe HVAC system under various interior and exterior environmentalconditions. A current maximum allowed ventilation rate that can beachieved without causing the HVAC system to compromise on any of the oneor more comfort conditions of the building space as indicated at block46. In some cases, determining the current maximum allowed ventilationrate includes inputting to the learned environmental model of thebuilding space one or more current comfort parameters of the buildingspace, one or more current interior environmental conditions and one ormore current exterior environmental conditions. In some cases,determining the current maximum allowed ventilation rate may includeinputting to the learned environmental model one or more of atemperature setpoint schedule, a current humidity setpoint, a currenttemperature within the building space, a current fan status of a fan ofthe HVAC system, a current valve status of a valve of the HVAC system,and/or a current load on the HVAC system. These are just examples. Theoutdoor air ventilation damper 18 of the HVAC system 16 is thencontrolled to provide ventilation up to and/or at the current maximumallowed ventilation rate, as indicated at block 48.

In some cases, the learned environmental model may be configured topredict a current heating capacity of the HVAC system, a current coolingcapacity of the HVAC system, a load on the HVAC system, and/or any othersuitable parameters. These parameters may be considered intermediateparameters that are used when determining the current maximum allowedventilation rate.

FIG. 3 is a flow diagram showing an illustrative method 60 for providinga maximum allowed ventilation rate for a building space (such as thebuilding space 12) while maintaining adherence to comfort parametersduring a proscribed period of time using a Heating, Ventilating and/orAir Conditioning (HVAC) system (such as the HVAC system 16) of thebuilding space. The illustrative method 60 includes tracking one or moreinterior environmental conditions within the building space and one ormore exterior environmental conditions outside of the building spaceduring operation of the HVAC system, as indicated at block 62. The oneor more interior environmental conditions may include indoor airtemperature. The one or more interior environmental conditions mayinclude indoor humidity. The one or more interior environmentalconditions may include concentrations of one or more indoor pollutantssuch as but not limited to CO₂, particular matter (PM 2.5 and PM 10) andvolatile organic compounds (VOCs). The one or more interiorenvironmental conditions may include occupancy such as a people countand/or people density in the building. The one or more exteriorenvironmental conditions may include outdoor air temperature. The one ormore exterior environmental conditions may include outdoor humidity. Theone or more exterior environmental conditions may include one or moreoutdoor pollutants. These are just examples.

In some cases, occupancy data may be tracked over time, as indicated atblock 64. Tracking occupancy data over time may include using videoanalytics to analyze one or more video streams in order to ascertainoccupancy. The video streams can come from security cameras, forexample. Tracking occupancy data over time may include using sensors todetect occupancy within a zone of the building space. In some instances,tracking occupancy data over time may include using sensors to detectindividuals entering a zone of the building space and to detectindividuals exiting a zone of the building space. For example, a time offlight (e.g. LIDAR) sensor may be used to detect and maintain a count ofpeople in the zone of the building space. From the people count, andusing the size of zone, a measure of people density for the zone can beestimated. Likewise, a milli-meter wave sensor (e.g. Radar) may be usedto detect and maintain a count of people in the zone of the buildingspace. These are just examples. People are known to produce contaminatesin the building space.

How one or more interior environmental conditions change in conjunctionwith changes in occupancy may be tracked, as indicated at block 66. Forexample, temperature, humidity, CO2, PM, VOCs and/or other parametersmay be tracked. An environmental model for the building space is learnedover time based at least in part on the tracked one or more interiorenvironmental conditions within the building space and the one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system under various conditions, as well as basedat least in part on the tracked occupancy data and how the one or moreinterior environmental conditions changes in conjunction with changes inoccupancy, as indicated at block 68.

The learned environmental model is configured to predict anenvironmental state of the building space in response to operation ofthe HVAC system under various interior and exterior environmentalconditions. A current maximum allowed ventilation rate that can beachieved without causing the HVAC system to compromise on any of the oneor more comfort conditions of the building space as indicated at block70. This may include inputting to the learned environmental model of thebuilding space one or more current comfort parameters associated withthe one or more comfort conditions of the building space, one or morecurrent interior environmental conditions (sometimes including currentoccupancy) and one or more current exterior environmental conditions. Insome cases, determining the current maximum allowed ventilation rate mayinclude inputting to the learned environmental model one or more of atemperature setpoint schedule, a current temperature within the buildingspace and a current building space occupancy. The outdoor airventilation damper of the HVAC system is then controlled to provideventilation up to or at the current maximum allowed ventilation rate, asindicated at block 72. In some cases, and as indicated at block 74, thelearned environmental model may be used to ascertain how the buildingspace is predicted to respond to a change in the ventilation rate inorder to determine the current maximum allowed ventilation rate.

FIG. 4 is a flow diagram showing an illustrative method 80 for providinga maximum allowed ventilation rate for a building space (such as thebuilding space 12) while not compromising on one or more comfortconditions in the building space using a Heating, Ventilating and/or AirConditioning (HVAC) system that includes an outdoor air ventilationdamper (such as the HVAC system 16) of the building space. Theillustrative method 80 includes tracking one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system, as indicated at block 82. The one or moreinterior environmental conditions may include indoor air temperature.The one or more interior environmental conditions may include indoorhumidity. The one or more interior environmental conditions may includeconcentrations of one or more indoor pollutants such as but not limitedto CO₂, particular matter (PM 2.5 and PM 10) and volatile organiccompounds (VOCs). The one or more interior environmental conditions mayinclude occupancy and/a people count in the building. The one or moreexterior environmental conditions may include outdoor air temperature.The one or more exterior environmental conditions may include outdoorhumidity. The one or more exterior environmental conditions may includeone or more outdoor pollutants. These are just examples.

An environmental model for the building space is learned over time basedat least in part on the tracked one or more interior environmentalconditions within the building space and the one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system, as indicated at block 84. A current maximum allowedventilation rate that allows adherence to one or more comfort parametersof the building space is determined as indicated at block 86. This mayinclude inputting to the environmental model of the building space oneor more current comfort parameters associated with the one or morecomfort conditions of the building space, one or more current interiorenvironmental conditions and one or more current exterior environmentalconditions. In some cases, determining the current maximum allowedventilation rate may include inputting to the environmental model one ormore of a temperature setpoint schedule, a current temperature withinthe building space and a current building space occupancy. In somecases, the learned environmental model may be configured to predict acurrent heating capacity of the HVAC system, a current cooling capacityof the HVAC system, a load on the HVAC system, and/or any other suitableparameters. These parameters may be considered intermediate parametersthat are used when determining the current maximum allowed ventilationrate. The outdoor air ventilation damper of the HVAC system is thencontrolled to provide ventilation at the current maximum allowedventilation rate, as indicated at block 88.

In some instances, as indicated at block 90, the method 80 may includeinitiating a purge period in which the HVAC system provides aventilation rate that is above the current maximum allowed ventilationrate for a period of time. In some cases, the purge period may provide acomplete or a substantially complete air replacement within the buildingspace. In some cases, the HVAC system is allowed to compromise on one ormore of the one or more comfort conditions of the building space duringat least part of the purge period. For example, the HVAC system may notcondition incoming fresh air during at least part of the purge period.In some cases, the purge period may end at a time prior to a start of aproscribed period of time sufficient to permit the HVAC system tocondition the building space and reach a comfort setting prior to thestart of the proscribed period of time. In some cases, such as in anoffice building, the purge period is performed at night when occupancyis not expected. In a movie theater, the purge period may be performedbetween shows. These are just examples.

FIG. 5 is a flow diagram showing an illustrative method 100 forproviding a dynamic ventilation rate for a building space using aHeating, Ventilating and/or Air Conditioning (HVAC) system. Theillustrative method 100 includes tracking over time one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system, as indicated at block 102. The one or moreinterior environmental conditions may include indoor air temperature.The one or more interior environmental conditions may include indoorhumidity. The one or more interior environmental conditions may includeconcentrations of one or more indoor pollutants such as but not limitedto CO₂, particular matter (PM 2.5 and PM 10) and volatile organiccompounds (VOCs). The one or more interior environmental conditions mayinclude occupancy and/a people count in the building. The one or moreexterior environmental conditions may include outdoor air temperature.The one or more exterior environmental conditions may include outdoorhumidity. The one or more exterior environmental conditions may includeone or more outdoor pollutants. These are just examples.

An environmental model for the building space is learned over time basedat least in part on the tracked one or more interior environmentalconditions within the building space and the one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system. The learned environmental model may be configured topredict an environmental state of the building space in response tooperation of the HVAC system under various interior and exteriorenvironmental conditions, as indicated at block 104. A dynamicventilation rate for the HVAC system of the building space is determinedbased at least in part on inputting to the environmental model of thebuilding space one or more current interior environmental conditions andone or more current exterior environmental conditions, as indicated atblock 106. The outdoor air ventilation damper of the HVAC system is thencontrolled to provide ventilation to the building space at thedetermined dynamic ventilation rate, as indicated at block 108.

In some instances, the environmental model may predict a current heatingcapacity of the HVAC system. The environmental model may predict acurrent cooling capacity of the HVAC system. The environmental model maypredict a current and/or expected load on the HVAC system. In somecases, the environmental model may predict the HVAC systems capacity tocool/heat the outdoor air given the predicted load and outside weatherconditions. In some cases, a maximum ventilation parameter is determinedthat is representative of a rate of outside air having the one or moreexterior environmental conditions that can be conditioned by the measureof current unused heating and/or cooling capacity of the HVAC systemwhile still maintaining the one or more comfort conditions for thebuilding space. The dynamic ventilation rate may then be based at leastin part on the maximum ventilation parameter. For example, the dynamicventilation rate may be capped at by the maximum ventilation parameter,where the maximum ventilation parameter may be representative of amaximum ventilation rate that can be achieved given the HVAC systemscurrent capacity to cool/heat the outdoor air given the predicted loadand outside weather conditions. In some cases, a certain capacity marginmay be in-built into the maximum ventilation parameter.

In some cases, the tracking step (block 102), the learning step (block104) and the determining step (block 106) may be performed at a remotelocation remote from the building space (e.g. at server 30 of FIG. 1 ),while the controlling step (block 108) may be performed by the HVACsystem (e.g. HVAC system 16 of FIG. 1 ) of the building space. However,it is contemplated that the tracking step (block 102), the learning step(block 104) and/or the determining step (block 106) may be performed onsite (e.g. by the controller 14 and/or edge controller 32 of FIG. 1 ).These are just examples.

FIG. 6 is a flow diagram showing an illustrative method 120 forproviding a dynamic ventilation rate for a building space using aHeating, Ventilating and/or Air Conditioning (HVAC) system. Theillustrative method 120 includes tracking over time one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system, as indicated at block 122. The one or moreinterior environmental conditions may include indoor air temperature.The one or more interior environmental conditions may include indoorhumidity. The one or more interior environmental conditions may includeconcentrations of one or more indoor pollutants such as but not limitedto CO₂, particular matter (PM 2.5 and PM 10) and volatile organiccompounds (VOCs). The one or more interior environmental conditions mayinclude occupancy and/a people count in the building. The one or moreexterior environmental conditions may include outdoor air temperature.The one or more exterior environmental conditions may include outdoorhumidity. The one or more exterior environmental conditions may includeone or more outdoor pollutants. These are just examples.

An environmental model for the building space is learned over time basedat least in part on the tracked one or more interior environmentalconditions within the building space and the one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system. The environmental model may be configured to predictan environmental state of the building space in response to operation ofthe HVAC system under various interior and exterior environmentalconditions, as indicated at block 124. In this illustrative method, acurrent occupancy count and/or density of the building space may bemaintained by counting occupants entering and existing the buildingspace, as indicated at block 126.

Tracking occupancy data over time may include using video analytics toanalyze one or more video streams in order to ascertain occupancy. Thevideo streams can come from security cameras, for example. Trackingoccupancy data over time may include using sensors to detect occupancywithin a zone of the building space. In some instances, trackingoccupancy data over time may include using sensors to detect individualsentering a zone of the building space and to detect individuals exitinga zone of the building space. For example, a time of flight (e.g. LIDAR)sensor may be used to detect and maintain a count of people in the zoneof the building space. Likewise, a milli-meter wave sensor (e.g. Radar)may be used to detect and maintain a count of people in the zone of thebuilding space. These are just examples. People are known to producecontaminates in the building space.

A dynamic ventilation rate for the HVAC system of the building space isdetermined based at least in part on inputting to the environmentalmodel of the building space one or more current interior environmentalconditions and one or more current exterior environmental conditions.The dynamic ventilation rate may be compensated based on the currentoccupancy count and/or density of the building space, as indicated atblock 128. The outdoor air ventilation damper of the HVAC system is thencontrolled to provide ventilation to the building space at thedetermined dynamic ventilation rate, as indicated at block 130. In somecases, the dynamic ventilation rate is capped to allow the HVAC systemto not compromise on one or more comfort conditions in the buildingspace.

FIG. 7 is a flow diagram showing an illustrative method 140 forproviding a dynamic ventilation rate for a building space using aHeating, Ventilating and/or Air Conditioning (HVAC) system. Theillustrative method 140 includes tracking over time one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system, as indicated at block 142. The one or moreinterior environmental conditions may include indoor air temperature.The one or more interior environmental conditions may include indoorhumidity. The one or more interior environmental conditions may includeconcentrations of one or more indoor pollutants such as but not limitedto CO₂, particular matter (PM 2.5 and PM 10) and volatile organiccompounds (VOCs). The one or more interior environmental conditions mayinclude occupancy and/a people count in the building. The one or moreexterior environmental conditions may include outdoor air temperature.The one or more exterior environmental conditions may include outdoorhumidity. The one or more exterior environmental conditions may includeone or more outdoor pollutants, for example.

An environmental model for the building space is learned over time basedat least in part on the tracked one or more interior environmentalconditions within the building space and the one or more exteriorenvironmental conditions outside of the building space during operationof the HVAC system. The environmental model is configured to predict anenvironmental state of the building space in response to operation ofthe HVAC system under various interior and exterior environmentalconditions, as indicated at block 144.

A dynamic ventilation rate for the HVAC system of the building space isdetermined based at least in part on inputting to the environmentalmodel of the building space one or more current interior environmentalconditions and one or more current exterior environmental conditions, asindicated at block 146. The outdoor air ventilation damper of the HVACsystem is then controlled to provide ventilation to the building spaceat the determined dynamic ventilation rate, as indicated at block 148.

In some instances, as indicated at block 150, the method 140 may includeinitiating a purge period outside of a proscribed period of time inwhich the HVAC system provides a ventilation rate that is above thedynamic ventilation rate for a period of time. In some cases, the purgeperiod may provide a complete or a substantially complete airreplacement within the building space. In some cases, the HVAC system isallowed to compromise on one or more of the one or more comfortconditions of the building space during at least part of the purgeperiod. For example, the HVAC system may not condition incoming freshair during at least part of the purge period. In some cases, the purgeperiod may end at a time prior to a start of a proscribed period of timesufficient to permit the HVAC system to condition the building space andreach a comfort setting prior to the start of the proscribed period oftime. In some cases, such as in an office building, the purge period isperformed at night when occupancy is not expected. In a movie theater,the purge period may be performed between shows. These are justexamples.

FIG. 8 is a schematic block diagram showing an illustrative HVACventilation control system 160. The ventilation control system 160includes an illustrative computational algorithm 162 that receives anumber of real or near-real time data inputs 164 and a number of userconfigurations 166 and provides a control output 168. In this example,the real time data inputs 164 include one or more of inside temperature,inside humidity, inside PM2.5 (particulate matter), TVOC (total volatileorganic compounds), outdoor temperature, outdoor humidity, outdoorpollutants, occupancy density and the area of a particular indoor space.The user configurations 166 include one or more of temperaturesetpoints, schedules and mode selection. The mode selection may beselected from, for example, a pandemic mode calling for a maximumallowed ventilation rate while not compromising on one or more comfortconditions in the building space (e.g. still maintaining adherence tocomfort parameters), and a normal operating mode calling for a minimumventilation rate while still adequately ventilating the building giventhe current contaminates or expected contaminates in the building.

In this example, the control output 168 can include not only ventilationcontrol, but in some instances can identify anomalies in the HVACsystem. An anomaly may be detected when the HVAC system behaves in amanner that is different or even substantially different from what isexpected. The control output 168 may include a message to a useralerting the user of the anomaly and may include one or more possiblecauses for the anomaly. An example of an anomaly would be expecting acurrent temperature to be reached at a given time, but discovering thatthe actual current temperature has not reached by the expectedtemperature. Possible causes could be a cooling valve malfunction or aheating valve malfunction. A belt driving a fan may have stretched oreven broken. These are just examples. Based on the nature of theanomaly, the model may indicate one or more possible root causes for theuser to investigate.

The illustrative computational algorithm 162 may be used in part tocreate, edit or update the environmental model. Accordingly, theillustrative computational algorithm 162 may have a variety of inputssuch as but not limited to one or more interior environmental conditionswithin the building space and one or more exterior environmentalconditions outside of the building space during operation of the HVACsystem. Block 170 of FIG. 9 provides additional examples of possibledata sources. Data sources may include environmental parameters, airquality values, building data and equipment type data. Block 172 of FIG.9 provides for the algorithm understanding the data. This may include aquantitative definition of Contaminants of Concern (CoC) as well as adefinition of comfort standards. Data understanding may also includeunderstanding the capacity and efficiency of the HVAC equipment, forexample. Block 174 of FIG. 9 pertains to data collection. Datacollection may include all of the sources listed in the block 170. Datacollection may include ground truth (reference) information for CoC,comfort and possible anomalies. In some cases, data collection may spana period of at least one year, in order to capture seasonal variations.

The illustrative computational algorithm 162 has a Step One, asindicated at block 176 of FIG. 9 . In Step One, the illustrativecomputational algorithm 162, particularly when in pandemic mode, has agoal of maximizing ventilation (or minimizing CoC concentration in thebuilding) while meeting comfort requirements and staying within thecapabilities and efficiencies of the HVAC system. In some cases, theillustrative computational algorithm 162 may have a goal, particularlywhen not in pandemic mode, of providing an appropriate dynamicventilation rate such as to minimize the ventilation rate while stilladequately ventilating the building given the current contaminates orexpected contaminates in the building. Step Two, indicated at block 178,has objectives that include deriving performance metrics for acceptableCoC values. Step Two includes paying attention to comfort requirements,test data sets for validation, model prediction accuracies and modelreal time deployment requirements.

The illustrative computational algorithm 162 has a Step Three, asindicated at block 180. Step Three involves data preparation. This mayinclude data quality or pre-processing steps in order to check formissing data, unusual or unexpected values, and the like. A Step Four,as indicated at block 182, involves anomaly detection. For example,statistical and/or model-based approaches may be used to detect sensorfaults, equipment failures, clogged filters and the like. Step Five,indicated at block 184 of FIG. 9 , includes feature engineering. Thismay include selecting variables of interest. This may includecorrelation studies for CoC and variables that can or have been found toinfluence CoC values. This may include correlation studies for comfortand various environmental parameters and equipment data values.

The illustrative computational algorithm 162 has a Step Six, asindicated at block 186. Step Six involves model building. Model buildingmay, for example, include one or more regression models that aim topredict CoC as a function of air quality, environmental parameters andbuilding data. Model building may include a dynamic ML (machinelearning) model that aims to predict appropriate outdoor air intake intothe building as a function of equipment data, building data andenvironmental parameters. Step Seven, indicated at block 188, pertainsto model validation. This may include cross-validation of model accuracywith respect to the objectives defined in Step Two (block 178). StepEight, indicated at block 190, pertains to continuous learning by theillustrative computational algorithm 162. Data may be collected duringdeployment and processing, as indicated at Step Three (block 180) andStep Four (block 182). Batch mode learning and model updating based onStep Six (block 186) may also occur. The illustrative computationalalgorithm 162 may reside within the controller 14, the edge controller32 and/or the cloud-based server 30, for example.

In some cases, the HVAC system 16 (FIG. 1 ) may be operated inaccordance with a selected mode out of a plurality of modes. Forexample, the HVAC system 16 may be operated in accordance with a Healthmode, which prioritizes providing as much fresh air as is feasible tomaximize air quality parameters while maintaining comfort in thebuilding space. The Health mode may be selected during a pandemic, forexample. In some cases, the ventilation protocols described herein withrespect to FIGS. 2 through 9 may be considered as being applicable to aHealth mode. As another example, the HVAC system 16 may be operated inaccordance with an Energy mode, which prioritizes minimizing energycosts for running the HVAC system while maintaining comfort and airquality parameters. As another example, the HVAC system 16 may beoperated in accordance with a Productivity mode, which prioritizesminimizing energy while reaching higher air quality limits than thoserequired by the Energy mode, as it has been found that reachingparticular air quality limits above those required for safety ofbuilding occupants can generate improvement in employee productivity.These are just examples of contemplated ventilation modes.

When in the Health mode, a goal may be to maximize outdoor air intakewithout compromising thermal comfort requirements within the buildingspace 12, or within a zone within the building space 12. It will beappreciated that various constraints may be taken into account,including but not limited to HVAC equipment capacity, zone-wiseoccupancy levels, outdoor temperature and/or other considerations. Insome cases, it may not be beneficial to bring in more outdoor airbecause occupant comfort may suffer and/or it energy costs associatedwith operating the HVAC system may substantially increase with littlebenefit. In other cases, when the outdoor air is at a temperature and/orhumidity that requires little or no conditioning, it may be beneficialto bringing in more outdoor air to help meet temperature and otherindoor comfort conditions.

In some cases, when in Health mode, zone level indoor air quality is notdirectly constrained with respect to CO₂ concentration, PM2.5concentration and TVOC (total volatile organic compound) concentration.Rather, it is understood that these concentrations will naturally dropbelow IAQ limits simply by bringing in sufficient fresh air fromoutdoors. Of course, if the outdoor air is particularly polluted on aparticular day or time of day, this may impact how much fresh air shouldbe brought in.

In order to optimize ventilation and other features of operating theHVAC system 16, there is a goal of minimizing a cost function that tiestogether the competing interests in operating the HVAC system 16. In theHealth mode, there is a desire for as much fresh air as possible whilestill maintaining comfort requirements and minimizing energyconsumption, all at the same time. The illustrative cost functionincludes terms for each of a variety of competing requirements. Forexample, there is a cost function term for air flow rate, includingbeing able to maximize airflow into a particular AHU (air handling unit)as well as being able to maximize airflow into a particular zone. Thereis a cost function term for temperature, that only penalizes the costfunction when zone temperature constraints are not met. In some cases,the cost function term for temperature involves slack variables. Thereis a cost function term for energy, that only penalizes the costfunction once the AHU has met maximum airflow requirements. In somecases, the cost function term for temperature and the cost function termfor energy are weighted, and the problem can be formulated in twodifferent ways, based on how the cost function term for energy isweighted in the optimization function.

An illustrative cost function for a Formulation 1 of the Health mode isgiven below:

${J = {{Min}\left\lbrack {{\sum\limits_{k = 0}^{N - 1}\left( {{\overset{\_}{x}}_{k}^{f} - x_{k}^{f}} \right)} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}{\left( \frac{{Occ}_{i}}{{\overset{\_}{Occ}}_{i}} \right)\left( \frac{{\overset{\_}{x}}_{i,k}^{f} - x_{i,k}^{f}}{{\overset{\_}{x}}_{i,k}^{f}} \right)}}} - {\left( {1 - {h\left( {\overset{\_}{x}}_{k}^{f} \right)}} \right){\sum\limits_{k = 0}^{N_{c} - 1}{{R_{k}{l\left( d_{k} \right)}}}_{1}}} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{k = 0}^{N_{z}}{{Q_{k}{f\left( z_{k,i} \right)}}}_{2}^{2}}}} \right\rbrack}}{{where}:}{\sum\limits_{k = 0}^{N - 1}\left( {{\overset{\_}{x}}_{k}^{f} - x_{k}^{f}} \right)}$This term represents the airflow rate at the AHU level;

$\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}{\left( \frac{{Occ}_{i}}{{\overset{\_}{Occ}}_{i}} \right)\left( \frac{{\overset{\_}{x}}_{i,k}^{f} - x_{i,k}^{f}}{{\overset{\_}{x}}_{i,k}^{f}} \right)}}$This term represents the air flowrate at the zone level and normalizesthe airflow rate;Σ_(k=0) ^(N) ^(c) ⁻¹ ∥R _(k) l(d _(k))∥₁This term represents the cost associated with energy expenditure;Σ_(k=0) ^(N−1)Σ_(k=0) ^(N) ^(z) ∥Q _(k)ƒ(z _(k,i))∥₂ ²This term represents the cost associated with thermal comfortviolations; and (1−h(x _(k) ^(ƒ))) is a weighting term that gives atransition from a low weight at the start to a high value on satisfyinga threshold. This is illustrated in FIG. 10 , which is a graph showingenergy penalty weight (ranging from 0 to −1) versus air flow rate. Ascan be seen, the energy penalty weight remains at or close to zero untilthe air flow rate approaches a maximum air flow rate.

In particular:

(x _(k) ^(ƒ)−x_(k) ^(ƒ)) denotes the differential air flow rate withrespect to the maximum airflow rate given by x _(k) ^(ƒ);

(x _(i,k) ^(ƒ)−x_(i,k) ^(ƒ)) denotes the differential air flow rate inzone “i” with respect to the maximum design flowrate for the zone givenby x _(i,k) ^(ƒ);

x_(i,k) ^(ƒ) is a function of actuator manipulations (a_(k)) and thesetpoints (r_(k)); the decision variables (d_(k)) include a_(k) andr_(k);

l(d_(k)) denotes the energy consumed as a function of the setpoint r_(k)and the actuator movement a_(k).

It will be appreciated that the illustrative cost function ofFormulation 1 of the Health mode is subject to a number of constraintslisted below:

-   -   Constraints (s.t.)    -   x(k+1)=Ax(k)+Bu(k)+Ww(k) (System dynamics)    -   x(0)=x₀ (Initial state estimate: y→[obsrver]→x)    -   Gd_(k)≤g (Constraint on the decision variables)    -   HΔd_(k)≤h (Constraints on the rate change of decision variables)    -   where, d_(k)=[r_(k), a_(k)]

${h\left( x^{f} \right)} = \frac{{\overset{\_}{x}}_{k}^{f} - {0.99x_{k}^{f}}}{{\overset{\_}{x}}_{k}^{f} - x_{k}^{f}}$

-   -   (observed states at each iteration should always satisfy:)    -   x _(k,i) ^(T)−z_(k,i)≤x_(k,i) ^(T)≤x _(k,i) ^(T)+z_(k,i)    -   0≤z_(k,i)≤z_(k,i)    -   where, iϵ[N_(z)]

In one example, the energy consumed at a heating coil of the HVAC systemdepends on the heat flows or the temperature gradient. This effect iscaused as a result of heating valve movement, for example. Thefunctional relationships among the variables are established as part ofan energy model, which is based on system dynamics.

It will be appreciated that ƒ(z_(k)) is a function of the slack variablez_(k) and is used to capture the cost related to thermal discomfort. Theformulation presented here is directed to maximizing the air flow ratesat the AHU as well as at the zone level, along with minimizing energycosts and thermal discomfort.

An illustrative cost function for a Formulation 2 of the Health mode isgiven below:

${J = {{Min}\left\lbrack {{\sum\limits_{k = 0}^{N - 1}\left( {{\overset{\_}{x}}_{k}^{f} - x_{k}^{f}} \right)} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}{\left( \frac{{Occ}_{i}}{{\overset{\_}{Occ}}_{i}} \right)\left( \frac{{\overset{\_}{x}}_{i,k}^{f} - x_{i,k}^{f}}{{\overset{\_}{x}}_{i,k}^{f}} \right)}}} - {\lambda{\sum\limits_{k = 0}^{N_{c} - 1}{{R_{k}{l\left( d_{k} \right)}}}_{1}}} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{k = 0}^{N_{z}}{{Q_{k}{f\left( z_{k,i} \right)}}}_{2}^{2}}}} \right\rbrack}}{{where}:}{\sum\limits_{k = 0}^{N - 1}\left( {{\overset{\_}{x}}_{k}^{f} - x_{k}^{f}} \right)}$This term is representative of the air flow rates at the AHU level;

$\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}{\left( \frac{{Occ}_{i}}{{\overset{\_}{Occ}}_{i}} \right)\left( \frac{{\overset{\_}{x}}_{i,k}^{f} - x_{i,k}^{f}}{{\overset{\_}{x}}_{i,k}^{f}} \right)}}$This term is representative of the air flow rates at the zone level;Σ_(k=0) ^(N) ^(c) ⁻¹ ∥R _(k) l(d _(k))∥₁This term is representative of the cost associated with energyexpenditure;Σ_(k=0) ^(N−1)Σ_(k=0) ^(N) ^(z) ∥Q _(k)ƒ(z _(k,i))∥₂ ²This term is representative of the cost associated with thermal comfortviolations; and

λ is a binary value that takes the value of one (1) when the fresh airflow rate at the AHU level is at maximum, and is otherwise set equal tozero (0).

It will be appreciated that the illustrative cost function ofFormulation 2 of the Health mode is subject to a number of constraintslisted below:

Constraints (s.t.)

x(k+1)=Ax(k)+Bu(k)+Ww(k) (System dynamics)

x(0)=x₀ (Initial state estimate: y→[obsrver]→x)

Gd_(k)≤g (Constraint on the decision variables)

HΔd_(k)≤h (Constraints on the rate change of decision variables)

where, d_(k)=[r_(k), a_(k)]

λϵ{0, 1} binary

(observed states at each iteration should always satisfy:)

x _(k,i) ^(T)−z_(k,i)≤x_(k,i) ^(T)≤x _(k,i) ^(T)+z_(k,i)

0≤z_(k,i)≤z_(k,i)

where, iϵ[N_(z)]

For completeness, a description for the notations used in theillustrative cost functions is provided below:

Notation Description

x: state Vector, xϵ

^(n) ^(z)

u: system input vector, uϵ

^(n) ^(u)

y: system output vector, yϵ

^(n) ^(y)

w: disturbance input vector, wϵ

^(n) ^(w)

x₀: initial state vector, x₀ϵ

^(n) ^(x)

n_(x): dimension of the state vector

n_(u): dimension of the system input vector

n_(y): dimension of the system output vector

n_(y): dimension of the disturbance input vector

A: state-space matrix, AϵR^(n) ^(x) ^(*n) ^(x)

B: input matrix, BϵR^(n) ^(z) ^(*n) ^(z)

W: disturbance matrix, BϵR^(n) ^(z) ^(*n) ^(z)

C: output matrix, CϵR^(n) ^(x) ^(*n) ^(x)

T_(s): sampling time

N_(c): control horizon in time steps, control horizon window=N_(c)T_(s)

N: prediction horizon in time steps, prediction horizon window=NT_(s)

N_(z): number of zones in the multi-zone building

[N_(z)]: set {1, 2, . . . , N_(z)}

Notation Description

x_(k,i) ^(IAQ): IAQ concentration at the k^(th) instant for zone t

z_(k,i): slack variable on the i^(th) zone temperature at the k^(th)instant

s_(k,i): slack variable on the i^(th) zone IAQ concentration at thek^(th) instant

z _(k,i): upper bound on the slack variable z on the i^(th) zone at thek^(th) instant

s _(k,i): upper bound on the slack variable s on the i^(th) zone at thek^(th) instant

z _(i,k) ^(IAQ,A): upper bound on the IAQ concentration defined as perthe ASHRAE standard 62.1 & 62.2

x _(i,k) ^(IAQ,P): dynamic upper bound on the IAQ concentration for zonei at the k^(th) instant

x _(i,k) ^(IAQ): upper bound on the IAQ concentration in zone i at thek^(th) instant^(†)

x _(i,k) ^(IAQ): lower bound on the IAQ concentration in zone i at thek^(th) instant^(‡)

Notation Description

x_(k,i) ^(T): temperature for the i^(th) zone at the k^(th) instant

J: cost function

∥(.)∥₁: 1—norm of (.)

∥(.)∥₂: 2—norm of (.)

ƒ(z_(k,i)): function defining thermal comfort violation for the i^(th)zone at instant k

h(s_(k,i)): function defining IAQ bound violation for the i^(th) zone atinstant k

R_(k): weighing matrix for the MVs

Q_(k): weighing matrix for the thermal comfort violation

P_(k): weighing matrix for the CO₂ constraint violation

g: constraints on the decision variables, gϵ

^(n) ^(x)

h: constraints on the rate change of decision variable, hϵ

^(n) ^(x)

d_(k): decision variables at instant k

r_(k): set-points at instant k

a_(k): actuator manipulations at instant k

x _(k,i) ^(T): lower bound on the temperature for the i^(th) zone at thek^(th) instant

x _(k,i) ^(T): upper bound on the temperature for the i^(th) zone at thek^(th) instant

Occ_(k,i): occupancy in the i^(th) zone at the k^(th) instant

Occ _(k,i): peak occupancy in the i^(th) zone at the k^(th) instant

In the energy savings mode, control of the fresh air intake is coupledwith zone occupancy. Directly sensing occupancy provides a moreimmediate indication that CO₂ concentrations will start increasing. Itwill be appreciated that it takes some time for occupancy to result inincreasing CO₂ concentrations, hence directly measuring occupancy(rather than using CO₂ concentrations as a proxy for occupancy) is moreresponsive.

In some cases, ventilation strategies are evaluated to meet the IAQrequirements when there is least impact on energy consumption and it ismost favorable for equipment life expectancy. For example, it may bebetter to bring in additional fresh air when a building is not occupied,if the outdoor air is cooler and thus requires minimal conditioning inorder to meet comfort requirements.

In some cases, ventilation strategies are evaluated to minimize the costof energy that is consumed by each of the components of the HVAC system16. In some cases, a thermal comfort band may be used as a constraint,with a penalty on the thermal comfort violation by using a slackvariable on the zone temperature. Also, IAQ concentrations may be usedas constraints. In some cases, a penalty may be placed on the IAQ boundviolation by using a slack variable on IAQ variables (CO₂, PM2.5 andTVOC) in the zone.

An illustrative cost function for the energy savings mode is givenbelow:

${J = {{Min}\left\lbrack {{\sum\limits_{k = 0}^{N_{c} - 1}{{R_{k}{l\left( d_{k} \right)}}}_{1}} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}\left( {{{Q_{k}{f\left( z_{k,i} \right)}}}_{2}^{2} + {{P_{k}{h\left( s_{k,i} \right)}}}_{2}^{2}} \right)}}} \right\rbrack}}{{where}:}{\sum\limits_{k = 0}^{N_{c} - 1}{{R_{k}{l\left( d_{k} \right)}}}_{1}}$This term is the cost associated with energy expenditure;Σ_(k=0) ^(N−1)Σ_(i=0) ^(N) ^(z) ∥Q _(k)ƒ(z _(k,i))∥₂ ²This term represents the cost associated with thermal comfortviolations; andΣ_(k=0) ^(N−1)Σ_(i=0) ^(N) ^(z) ∥P _(k) h(s _(k,i))∥₂ ²This term represents the cost associated with IAQ variables boundviolations.

It will be appreciated that the illustrative cost function for theenergy savings mode is subject to a number of constraints listed below:

Constraints (s.t.)

x(k+1)=Ax(k)+Bu(k)+Ww(k) (System dynamics)

x(0)=x₀ (Initial state estimate: y→[obsrver]→x)

Gd_(k)≤g (Constraint on the decision variables)

HΔd_(k)≤h (Constraints on the rate change of decision variables)

where, d_(k)=[r_(k), a_(k)]

(observed states at each iteration should always satisfy:)

x _(k,i) ^(T)−z_(k,i)≤x_(k,i) ^(T)≤x _(k,i) ^(T)+z_(k,i)

0≤x_(k,i) ^(IAQ)≤x _(k,i) ^(IAQ,A)+s_(k,i)

0≤z_(k,i)≤z_(k,i)

0≤s_(k,i)≤s_(k,i)

where, iϵ[N_(z)]

Expect, Guaranteed feasibility.

In the productivity mode, zone-wise IAQ concentrations (and any othercontaminants of concern) are maintained at least to the levels definedby ASHRAE. It is known that productivity of occupants of a building mayincrease with lower IAQ concentrations. Using zone-wise control for IAQlevels means considering zone-wise occupancy levels, environmentalfactors and optimizing energy consumption. For example, if a first zoneis unoccupied while a second zone is at capacity, the second zone willreceive more fresh air, if not substantially more fresh air, relative tothe first zone. In some cases, IAQ concentrations within the zones maybe measured and used as another control feature.

An illustrative cost function for the productivity mode is as follows:

$\left. {J = {{{{Min}\left\lbrack {{\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{z}}\left( {x_{k}^{IAQ} - {\overset{\_}{x}}_{k}^{{IAQ},P}} \right)}} - \left( {1 - h - {\overset{\_}{x}}_{I,k}^{IAQ}} \right)} \right)}{\sum\limits_{k = 0}^{N_{c} - 1}{{R_{k}{l\left( d_{k} \right)}}}_{1}}} + {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{k = 0}^{N_{z}}\left( {{{Q_{k}{f\left( z_{k,i} \right)}}}_{2}^{2} + {{P_{k}{o\left( s_{k,i} \right)}}}_{2}^{2}} \right)}}}} \right\rbrack{{where}:}{\sum\limits_{k = 0}^{N - 1}{\sum\limits_{i = 1}^{N_{Z}}\left( {x_{k}^{IAQ} - {\overset{\_}{x}}_{k}^{{IAQ},P}} \right)}}$This term pertains to meeting IAQ thresholds within each zone as per theset IAQ standards in the productivity mode, where the IAQ thresholdsadjust dynamically as occupancy changes;(1−h(x _(i,k) ^(IAQ)))This term pertains to minimizing energy cost expenditure as thecumulative IAQ measures in various zones reaches close to the thresholdIAQ. This can be seen in FIG. 11 , which is a graph of energy penaltyweight (ranging from 0 to −1) versus cumulative IAQ concentrations. Ascan be seen, the energy penalty weight remains at or close to zero untilthe cumulative IAQ concentration becomes high. Alternatively, thefunction h(.) could also be expressed as a function of the IAQconcentrations in the return air duct (e.g. sensed by a sensor). In somecases, however, this may result in passive control of the energy term.

Moreover:Σ_(k=0) ^(N) ^(c) ⁻¹ ∥R _(k) l(d _(k))∥₁This term pertains to the cost associated with energy consumption;Σ_(k=0) ^(N−1)Σ_(i=1) ^(N) ^(z) ∥Q _(k)ƒ(z _(k,i))∥₂ ²This term pertains to the cost associated with thermal comfortviolations; andΣ_(k=0) ^(N−1)Σ_(i=1) ^(N) ^(z) P _(k) o(s _(k,i))This term pertains to the cost associated with IAQ variables boundviolations.

It will be appreciated that the illustrative cost function for theproductivity mode is subject to a number of constraints listed below:

where,x _(i,k) ^(IAQ,P)=ψ( Occ _(i) ,Occ _(i) ,x _(i,k) ^(IAQ) ,x _(i,k)^(IAQ))

-   -   The lower and upper limits on the IAQ contaminants concentration        for the productivity mode, i.e., x _(i,k) ^(IAQ) and x _(i,k)        ^(IAQ), respectively, is set by the operator.    -   One of the possible descriptions for x _(i,k) ^(IAQ,P) expressed        as an affine function is given as:

${\overset{\_}{x}}_{i,k}^{{IAQ},P} = {{\underline{x}}_{i,k}^{IAQ} + {\left( {{\overset{\_}{x}}_{i,k}^{{IAQ},P} - {\underline{x}}_{i,k}^{IAQ}} \right)\frac{{Occ}_{i,k}}{{\overset{\_}{Occ}}_{i,k}}}}$

-   -   However, the function could also be a non-linear map between the        Occupancy and contaminant concentration.    -   The function which weights the energy objective as a function of        the IAQ variables concentration is expressed as:

${h\left( x_{i,k}^{IAQ} \right)} = \frac{{\sum\limits_{i = 1}^{N_{z}}{\overset{\_}{x}}_{i,k}^{{IAQ},P}} - {0.99{\sum\limits_{i = 1}^{N_{z}}x_{i,k}^{IAQ}}}}{{\sum\limits_{i = 1}^{N_{z}}{\overset{\_}{x}}_{i,k}^{{IAQ},P}} - {\sum\limits_{i = 1}^{N_{z}}x_{i,k}^{IAQ}}}$

It will be appreciated that the illustrative cost function for theproductivity mode is subject to a number of constraints listed below:

Constraints (s.t.)

x(k+1)=Ax(k)+Bu(k)+Ww(k) (System dynamics)

x(0)=x₀ (Initial state estimate: y→[obsrver]→x)

Gd_(k)≤g (Constraint on the decision variables)

HΔd_(k)≤h (Constraints on the rate change of decision variables)

where, d_(k)=[r_(k), a_(k)]

(observed states at each iteration should always satisfy:)

x _(k,i) ^(T)−z_(k,i)≤x_(k,i) ^(T)≤x _(k,i) ^(T)+z_(k,i)

0≤x_(k,i) ^(IAQ)≤x _(k,i) ^(IAQ,A)+s_(k,i)

0≤z_(k,i)≤z_(k,i)

0≤s_(k,i)≤s_(k,i)

where, iϵ[N_(z)]

Expect, Guaranteed feasibility.

In particular:

(x_(i,k) ^(IAQ)−x _(i,k) ^(IAQ,P)) denotes the differential between theIAQ measure (x_(i,k) ^(IAQ)) and the IAQ threshold (x _(i,k) ^(IAQ,P)),which is defined and set by the user as per the productivity mode forthe zone “i”;

x _(i,k) ^(IAQ,A) denotes the IAQ threshold limits defined in accordancewith the ASHRAE standards 62.1 and 62.2;

x_(i,k) ^(IAQ) is a function of actuator manipulations (a_(k)) and thesetpoints (r_(k));

The functional relationships among the variables are established as partof the energy model, which is based on system dynamics. ƒ(z_(k)) is afunction of the slack variable z_(k) and is used to capture the costrelated to thermal discomfort. o (s_(k)) is a function of the slackvariable s_(k) and is used to capture the cost related to IAQ limitviolations. As the thermal comfort and/or the IAQ contaminantconcentration band starts to be violated, the cost function starts topenalize the deviation from the corresponding band.

An objective is to minimize IAQ concentrations to the levels defined andset as per the productivity mode. In some cases, a cumulative sum of IAQconcentrations may be used to trigger energy minimization when thecontaminants reach close to the threshold defined as per theproductivity mode. Worst case IAQ levels to be met while in theproductivity mode may be those as defined per the ASHRAE standards.

It will be appreciated that the building space 12 may switch betweenmodes. This can be done as a hard switch or a soft switch. A hard switchinvolves switching directly from a first mode to a second mode, with nointervening positions or mode. This can result in a large demand forinstant control inputs. In some cases, a hard switch can damageequipment such as actuators over time if the specifications of the firstmode and the second mode are substantially different. In contrast, asoft switch means switching from the first mode to the second mode overa number of intermediate control steps. This provides a smoothertransition between the first mode and the second mode, and can thusincrease the life of building equipment such as actuators.

Soft switching involves switching from one mode to the other in asmoother manner by implementing intermediate MPC (model predictivecontrol) over a number of control steps. Switching between the initialmode and the final mode happens using a soft switching coefficient beta(β) that relates the input and state variables of the two modes:U ^(S) =βU _(I)+(1−β)U _(F)X ^(S) =βX _(I)+(1−β)X _(F)

where,

-   -   U^(S) and X^(S) are the input and state variables, respectively,        for intermediate MPC;    -   U_(I) and X_(I) are the input and state variables for the        initial mode;    -   U_(F) and X_(F) are the input and state variables for the final        mode.

The switching coefficient beta (β) is given by:

$\begin{matrix}{{\beta_{k}(i)} = {\left\lbrack {1 - \left( \frac{k + i - k_{b}}{\tau_{sw}} \right)} \right\rbrack{H\left( {1 - \left( \frac{k + i - k_{b}}{\tau_{sw}} \right)} \right)}}} & \end{matrix}$where,

H(.) is the Heaviside function, which as shown in FIG. 12 is a stepfunction that switches from 0 to 1 in the time interval τ_(sw);

switching starts at k_(b) and ends at k_(e); and

τ_(sw)=k_(b)−k_(e) is the switching interval.

FIG. 13 is a flow diagram showing an illustrative method 200 forproviding dynamic ventilation for a building space (such as the buildingspace 12) serviced by a Heating, Ventilating and Air Conditioning (HVAC)system (such as the HVAC system 16) with one or more componentsincluding an outdoor air ventilation damper (such as the damper 18). Themethod 200 includes selecting a ventilation mode from a plurality ofventilation modes, as indicated at block 202. The plurality ofventilation modes include a health ventilation mode that when selectedattempts to maximize ventilation to the building space subject to one ormore constraints including a constraint of maintaining one or morecomfort conditions in the building space, as indicated at block 202 a.The plurality of ventilation modes include an energy savings ventilationmode that attempts to minimize energy consumed by the HVAC system tocondition air supplied to the building space subject to one or moreconstraints including a constraint of maintaining one or more comfortconditions in the building space and a constraint to maintain IAQcontaminants in the building space below predetermined energy savingslimits, as indicated at block 202 b. The method 200 includes controllingone or more components of the HVAC system, including the outdoor airventilation damper, in accordance with the selected ventilation mode, asindicated at block 204.

In some cases, the plurality of ventilation modes includes aproductivity ventilation mode that when selected attempts to controlventilation to the building space to maintain IAQ contaminants in thebuilding space below predetermined productively limits subject to one ormore constraints including a constraint of maintaining one or morecomfort conditions in the building space. In some cases, at least one ofthe predetermined productively limits is below a corresponding one ofthe predetermined energy savings limits.

In some cases, when the health ventilation mode is selected, controllingthe one or more components of the HVAC system, including the outdoor airventilation damper, includes minimizing an overall cost associated witha ventilation term for maximizing outdoor air ventilation to thebuilding space, an energy term for minimizing energy associated withoutdoor air ventilation to the building space, and a comfort term forpenalizing a deviation from one or more comfort conditions in thebuilding space. In some instances, when the energy savings ventilationmode is selected, controlling the outdoor air ventilation damperincludes minimizing an overall cost associated with an energy term forminimizing energy associated with outdoor air ventilation to thebuilding space, a comfort term that penalizes a deviation from one ormore comfort conditions in the building space, and an Indoor Air Quality(IAQ) term that penalizes violations of one or more predetermined IAQlimits. In some cases, the comfort term and the IAQ term may eachinclude one or more slack variables.

FIG. 14 is a flow diagram showing an illustrative method 206 forproviding dynamic ventilation for a building space (such as the buildingspace 12) serviced by a Heating, Ventilating and Air Conditioning (HVAC)system (such as the HVAC system 16) with one or more componentsincluding an outdoor air ventilation damper (such as the damper 18). Themethod 206 includes selecting a ventilation mode from a plurality ofventilation modes, as indicated at block 208. In some cases, theventilation mode may be selected by an operator. In some instances, theventilation mode may be automatically selected in accordance with aprogrammed schedule.

The plurality of ventilation modes includes a first ventilation modethat attempts to minimize energy consumed by the HVAC system tocondition air supplied to the building space while maintaining one ormore IAQ contaminants in the building space below one or morecorresponding first predetermined limits, as indicated at block 208 a.The plurality of ventilation modes includes a second ventilation modethat attempts to minimize energy consumed by the HVAC system tocondition air supplied to the building space while maintaining one ormore IAQ contaminants in the building space below one or morecorresponding second predetermined limits, wherein at least one of thesecond predetermined limits is below a corresponding one of the firstpredetermined limits, as indicated at block 208 b. The method 206includes controlling one or more components of the HVAC system,including the outdoor air ventilation damper, in accordance with theselected ventilation mode, as indicated at block 210.

FIG. 15 is a flow diagram showing an illustrative method 212 forproviding dynamic ventilation for a building space (such as the buildingspace 12) serviced by a Heating, Ventilating and Air Conditioning (HVAC)system (such as the HVAC system 16) with an outdoor air ventilationdamper (such as the damper 18). The method 212 includes developing abuilding model for the building space, the building model including anon-linear representation of how one or more environmental parametersassociated with the building space is predicted to respond to changes inHVAC system operation under a plurality of different operatingconditions, as indicated at block 214. A current operating condition isidentified, as indicated at block 216. A linear approximation of thenon-linear building model at the current operating condition isdetermined, wherein the linear approximation approximates how one ormore environmental parameters associated with the building space ispredicted to respond to changes in HVAC system operation at the currentoperating condition, as indicated at block 218. One or more componentsof the HVAC system, including the outdoor air ventilation damper, arecontrolled via Predictive Control (PC) using the determined linearapproximation of the non-linear building model, as indicated at block220.

In some cases, and as indicated at block 222, the method 2112 mayfurther include subsequently identifying a new operating condition. Anew linear approximation of the non-linear building model at the newoperating condition is determined, wherein the linear approximationapproximates how one or more environmental parameters associated withthe building space is predicted to respond to changes in HVAC systemoperation at the new operating condition, as indicated at block 224. Oneor more components of the HVAC system, including the outdoor airventilation damper, are controlled via Predictive Control (PC) using thedetermined new linear approximation of the non-linear building model, asindicated at block 226.

In some cases, controlling the outdoor air ventilation damper via ModelPredictive Control (MPC) further includes predicting a future value forone or more Indoor Air Quality (IAQ) parameters in the building spaceusing the determined linear approximation of the non-linear buildingmodel, and controlling one or more components of the HVAC system,including the outdoor air ventilation damper, to control the one or moreIAQ parameters in the building space in accordance with one or morethresholds. In some cases, the one or more thresholds may include anASHRAE standard threshold for each of the one or more IAQ parameters.The one or more thresholds may include a determined value for each ofthe one or more IAQ parameters that is expected to produce an enhancedproductivity level of occupants in the building space.

In some cases, controlling one or more components of the HVAC system,including the outdoor air ventilation damper, via Predictive Control(PC) using the determined linear approximation of the non-linearbuilding model includes selecting a ventilation mode out of a pluralityof ventilation modes, each ventilation mode having a different costfunction, identifying the cost function for the selected ventilationmode, and controlling one or more components of the HVAC system,including the outdoor air ventilation damper, via Model PredictiveControl (MPC), wherein the MPC minimized the identified cost functionwhen controlling the one or more components of the HVAC system.

In some instances, one of the plurality of ventilation modes includes ahealth ventilation mode in which the Predictive Control (PC) maximizesventilation to the building space subject to one or more constraintsincluding a constraint of maintaining one or more comfort conditions inthe building space. The cost function for the health ventilation modeincludes minimizing an overall cost associated with a ventilation termfor maximizing outdoor air ventilation to the building space, an energyterm for minimizing energy associated with outdoor air ventilation tothe building space, and a comfort term for penalizing a deviation fromthe one or more comfort conditions in the building space.

In some instances, one of the plurality of ventilation modes includes anenergy savings ventilation mode in which the Predictive Control (PC)attempts to minimize energy consumed by the HVAC system to condition airsupplied to the building space subject to one or more constraintsincluding a constraint of maintaining one or more comfort conditions inthe building space and a constraint to maintain IAQ contaminants in thebuilding space below predetermined energy savings limits. The costfunction for the energy savings ventilation mode includes minimizing anoverall cost associated with an energy term for minimizing energyassociated with outdoor air ventilation to the building space, a comfortterm that penalizes a deviation from one or more comfort conditions inthe building space, and an indoor air quality (IAQ) term that penalizesviolations of one or more predetermined IAQ limits.

In some instances, one of the plurality of ventilation modes includes aproductivity ventilation mode in which the Predictive Control (PC)attempts to minimize energy consumed by the HVAC system to condition airsupplied to the building space subject to one or more constraintsincluding a constraint of maintaining one or more comfort conditions inthe building space and a constraint to maintain IAQ contaminants in thebuilding space below predetermined productively limits, wherein at leastone of the predetermined productively limits is below a correspondingone of the predetermined energy savings limits.

FIG. 16 is a schematic view of an illustrative MPC architecture 230 forbuilding control. The MPC architecture 230 may be used in controlling avariety of functions within a building 232. The building 232 mayrepresent the building space 12, for example. A building model 234 isbuilt to represent the building 232, and can be used via MPC control tomodel how the building 232 will respond to various inputs. The inputscan include control inputs to various building systems, such as but notlimited to the HVAC system 16. The inputs can also include theenvironmental conditions outside the building 232, such as but notlimited to wind, outdoor temperature, solar load, outdoor humidity,outdoor AIQ concentrations, and the like. Because the performance of thebuilding 232 may vary for a variety of reasons, the building model 234may not be static, but instead may be dynamic such that the buildingmodel 234 can be updated over time in order to represent and modelchanging conditions and parameters for the building 232. In some cases,the model structure of the building model 234 may remain fixed, but themodel parameters may evolve over time. This may result in an AdaptiveModel Predictive Control (AMPC) of the HVAC system, and in particular anAdaptive Model Predictive Control (AMPC) the ventilation provided by theHVAC system. In some cases, the model parameters of the building model234 may be updated using machine learning and/or artificialintelligence.

In some cases, the building model 234 is included in the MPC formulationas part of the constraints, namely the system dynamics show below:

-   -   Constraints (s.t.)    -   x(k+1)=A_(k)x(k)+B_(k)u(k)+W_(k)w(k) (System dynamics)    -   x(0)=x₀ (Initial state estimate: y→[obsrver]→x)    -   Gu_(k)≤g (Constraint on the MVs)    -   HΔu_(k)≤h (Constraints on the rate change of MVs)

It will be appreciated that the MPC architecture 230 includes a costfunction block 236, representing the cost functions described hereinwith respect to health mode, energy mode and productivity mode, forexample. A constraints block 238 represents and accounts for the variousconstraints described herein with respect to the constraints thataccompanied each of the cost functions. A Disturbances block 240represents changes (W) that can impact the building 232 and the buildingmodel 234. The illustrative MPC architecture 230 further includes aFuture Predictions block 242 that predicts the next iterative state, anOptimization block 244 that optimizes the cost function 236, an Inputsblock 246 for providing inputs determined by the optimization block 244to the HVAC system, and an Estimator block 248 for estimating thecurrent state (x) of the HVAC system based on the inputs (u) and theoutputs (y) of the HVAC system. During operation, at each time instance,an online optimization problem is solved by the Optimization block 244to obtain an optimal control input trajectory over a fixed futurehorizon. The first control input (u_(k)) from the predicted trajectoryis implemented. In the next time instant, the optimization problem isre-parametrized and solved with the new state estimate (x_(k+1)).

FIG. 17 is a graphical representation of how a building model mayinclude a non-linear representation of how one or more environmentalparameters associated with the building space is predicted to respond tochanges in HVAC system operation under a plurality of differentoperating conditions. In this particular example, temperature is plottedversus time along a plot line 250. It can be seen that the plot line 250is non-linear. In some cases, and as referenced for example in FIG. 13 ,once a current operating condition has been identified (temperature, inthis case), a linear approximation to the non-linear building model canbe utilized. Say that the current temperature is indicated at a graphpoint 252. A linear approximation 254, which for example may beconsidered as being a tangent line drawn through the graph point 252,may be used for enabling MPC control because MPC control requires alinear model. As the temperature changes, an updated linearapproximation is selected that corresponds to the updated temperature.

Having thus described several illustrative embodiments of the presentdisclosure, those of skill in the art will readily appreciate that yetother embodiments may be made and used within the scope of the claimshereto attached. It will be understood, however, that this disclosureis, in many respects, only illustrative. Changes may be made in details,particularly in matters of shape, size, arrangement of parts, andexclusion and order of steps, without exceeding the scope of thedisclosure. The disclosure's scope is, of course, defined in thelanguage in which the appended claims are expressed.

What is claimed is:
 1. A method for providing up to a maximumventilation for a building space while not compromising on one or morecomfort conditions in the building space using a Heating, Ventilatingand/or Air Conditioning (HVAC) system that includes an outdoor airventilation damper, the HVAC system including a heating and/or coolingcapacity, the method comprising: tracking one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system; learning an environmental model for thebuilding space over time based at least in part on the tracked one ormore interior environmental conditions within the building space and theone or more exterior environmental conditions outside of the buildingspace during operation of the HVAC system, wherein the environmentalmodel predicts an environmental state of the building space in responseto operation of the HVAC system under various interior and exteriorenvironmental conditions; predicting a current maximum allowedventilation rate of outside air having the one or more exteriorenvironmental conditions that can be conditioned by the HVAC systemwithout exceeding the heating and/or cooling capacity of the HVAC systemand without causing the HVAC system to compromise on any of the one ormore comfort conditions of the building space, wherein predicting thecurrent maximum allowed ventilation rate comprises inputting to theenvironmental model of the building space one or more comfort parametersassociated with the one or more comfort conditions of the buildingspace, one or more current interior environmental conditions and one ormore current exterior environmental conditions; and controlling theoutdoor air ventilation damper of the HVAC system to provide ventilationup to or at the current maximum allowed ventilation rate.
 2. The methodof claim 1, wherein the one or more interior environmental conditionsinclude one or more of indoor air temperature, indoor humidity andconcentrations of one or more indoor pollutants.
 3. The method of claim1, wherein the one or more exterior environmental conditions include oneor more of outdoor temperature, outdoor humidity and one or more outdoorpollutants.
 4. The method of claim 1, further comprising: trackingoccupancy of the building space over time; tracking how one or moreinterior environmental conditions change in conjunction with changes inoccupancy; and learning the environmental model for the building spaceover time based at least in part on the tracked occupancy of thebuilding space and how the one or more interior environmental conditionschanges in conjunction with changes in occupancy.
 5. The method of claim4, wherein tracking occupancy of the building space over time comprisesusing one or more of video analytics to analyze one or more videostreams to ascertain occupancy of the building space, using sensors todetect occupancy within a zone of the building space, and using sensorsto detect individuals entering and/or exiting a zone of the buildingspace.
 6. The method of claim 1, further comprises using theenvironmental model for the building space to predict how the one ormore comfort conditions in the building space is expected to respond toa change in a ventilation rate when predicting the current maximumallowed ventilation rate that can be achieved without exceeding theheating and/or cooling capacity of the HVAC system and without causingthe HVAC system to compromise on any of the one or more comfortconditions of the building space.
 7. The method of claim 6, whereinpredicting the current maximum allowed ventilation rate comprisesinputting to the environmental model one or more of: a temperaturesetpoint schedule; a current humidity setpoint; a current temperaturewithin the building space; a current fan status of a fan of the HVACsystem; a current valve status of a valve of the HVAC system; a currentheating and/or cooling load on the HVAC system; and wherein the currentmaximum allowed ventilation rate includes a built-in heating and/orcooling capacity margin.
 8. The method of claim 1, further comprisinginitiating a purge period in which the HVAC system provides aventilation rate that is above the current maximum allowed ventilationrate for a period of time.
 9. The method of claim 8, wherein the HVACsystem is allowed to compromise on one or more of the one or morecomfort conditions of the building space during at least part of thepurge period.
 10. The method of claim 8, wherein the purge period endsat a time prior to a start of a proscribed period of time sufficient topermit the HVAC system to condition the building space and reach the oneor more comfort conditions prior to the start of the proscribed periodof time.
 11. The method of claim 1, wherein the environmental modeldetermines a current unused heating and/or cooling capacity of the HVACsystem, and wherein the environmental model uses the determined currentunused heating and/or cooling capacity of the HVAC system to predict thecurrent maximum allowed ventilation rate that can be achieved withoutexceeding the heating and/or cooling capacity of the HVAC system andwithout causing the HVAC system to compromise on any of the one or morecomfort conditions of the building space.
 12. The method of claim 11,wherein the environmental model determines a current heating and/orcooling load on the HVAC system, and wherein the environmental modeluses the determined current heating and/or cooling load and thedetermined unused heating and/or cooling capacity of the HVAC system topredict the current maximum allowed ventilation rate that can beachieved without causing the HVAC system to exceed the heating and/orcooling capacity of the HVAC system and without causing the HVAC systemto compromise on any of the one or more comfort conditions of thebuilding space.
 13. A method for providing a dynamic ventilation ratefor a building space using a Heating, Ventilating and/or AirConditioning (HVAC) system that includes an outdoor air ventilationdamper, the method comprising: tracking over time one or more interiorenvironmental conditions within the building space and one or moreexterior environmental conditions outside of the building space duringoperation of the HVAC system; learning an environmental model for thebuilding space over time based at least in part on the tracked one ormore interior environmental conditions within the building space and theone or more exterior environmental conditions outside of the buildingspace during operation of the HVAC system, wherein the environmentalmodel predicts an environmental state of the building space in responseto operation of the HVAC system under various interior and exteriorenvironmental conditions; determining a dynamic ventilation rate for theHVAC system of the building space based at least in part on inputting tothe environmental model of the building space one or more interiorenvironmental conditions and one or more exterior environmentalconditions, wherein the determined dynamic ventilation rate is capped ata dynamic maximum allowed ventilation rate that is determined based atleast in part on the one or more exterior environmental conditions suchthat outside air drawn in through the outdoor air ventilation damper atthe maximum allowed ventilation rate with the one or more exteriorenvironmental conditions can be conditioned by the HVAC system withoutexceeding a heating and/or cooling capacity of the HVAC system whilemaintaining one or more comfort conditions in the building space; andcontrolling the outdoor air ventilation damper of the HVAC system toprovide ventilation to the building space at the determined dynamicventilation rate.
 14. The method of claim 13, further comprising:maintaining a measure of occupancy of the building space by countingoccupants entering and exiting the building space; and determining thedynamic ventilation rate comprises compensating the dynamic ventilationrate based on the measure of occupancy of the building space.
 15. Themethod of claim 14, wherein the measure of occupancy is determined usingone or more of video feed of an entrance to the building space and asensor capable of sensing individual occupants that are entering thebuilding space.
 16. The method of claim 13, wherein the one or moreinterior environmental conditions include one or more of indoor airtemperature, indoor humidity, and concentrations of one or more indoorpollutants.
 17. The method of claim 13, wherein the one or more exteriorenvironmental conditions include one or more of outdoor temperature,outdoor humidity and outdoor pollutants.
 18. The method of claim 13,further comprising initiating a purge period in which the HVAC systemprovides a ventilation rate that is above the cap of the dynamicventilation rate for a period of time.
 19. The method of claim 13,wherein the tracking, learning and determining steps are performed at alocation remote from the building space, and the controlling step isperformed by the HVAC system of the building space.
 20. A method forcontrolling an outdoor air ventilation damper of a Heating, Ventilatingand/or Air Conditioning (HVAC) system serving a building space of abuilding, the method comprising: receiving one or more comfortconditions for the building space, one or more exterior environmentalconditions of air outside of the building, and one or more operatingconditions of the HVAC system; identifying a measure of current unusedheating and/or cooling capacity of the HVAC system based at least inpart on the one or more operating conditions of the HVAC system;determining a maximum ventilation parameter that is representative of arate of outside air having the one or more exterior environmentalconditions that can be conditioned by the measure of current unusedheating and/or cooling capacity of the HVAC system while stillmaintaining the one or more comfort conditions for the building space;determining a ventilation rate that is based at least in part on themaximum ventilation parameter; and controlling the outdoor airventilation damper of the HVAC system to provide ventilation at or belowthe determined ventilation rate.